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Adding XGBoost, LightGBM & CatBoost modeling in JMP 'decision tree' menu

As you know, many of data scientist use XGBoost, LightGBM, and CatBoost (gradient boosting decision tree) to solve their problems by using Python.

Could you add these modelings in the decision tree menu (JMP) as well?

If it can be provided by JMP Script Language (JSL), it will be great as well. Thank you. : )

 

Reference: https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf

 

 

 

 

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Level III

Please add Extreme Gradient Boosting as mentioned in the above comment as well as Adaptive Boosting (AdaBoost). These 2 methods are very more accurate compared to most other techniques.